# from functools import partial
#
# import spconv
# import torch
# import torch.nn as nn
#
# from ...utils import common_utils
# from .spconv_backbone import post_act_block
#
#
# class SparseBasicBlock(spconv.SparseModule):
#     expansion = 1
#
#     def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None):
#         super(SparseBasicBlock, self).__init__()
#         self.conv1 = spconv.SubMConv3d(
#             inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key
#         )
#         self.bn1 = norm_fn(planes)
#         self.relu = nn.ReLU()
#         self.conv2 = spconv.SubMConv3d(
#             planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key
#         )
#         self.bn2 = norm_fn(planes)
#         self.downsample = downsample
#         self.stride = stride
#
#     def forward(self, x):
#         identity = x.features
#
#         assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim()
#
#         out = self.conv1(x)
#         out.features = self.bn1(out.features)
#         out.features = self.relu(out.features)
#
#         out = self.conv2(out)
#         out.features = self.bn2(out.features)
#
#         if self.downsample is not None:
#             identity = self.downsample(x)
#
#         out.features += identity
#         out.features = self.relu(out.features)
#
#         return out
#
#
# class UNetV2(nn.Module):
#     """
#     Sparse Convolution based UNet for point-wise feature learning.
#     Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al)
#     From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
#     """
#     def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs):
#         super().__init__()
#         self.model_cfg = model_cfg
#         self.sparse_shape = grid_size[::-1] + [1, 0, 0]
#         self.voxel_size = voxel_size
#         self.point_cloud_range = point_cloud_range
#
#         norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
#
#         self.conv_input = spconv.SparseSequential(
#             spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
#             norm_fn(16),
#             nn.ReLU(),
#         )
#         block = post_act_block
#
#         self.conv1 = spconv.SparseSequential(
#             block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
#         )
#
#         self.conv2 = spconv.SparseSequential(
#             # [1600, 1408, 41] <- [800, 704, 21]
#             block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
#             block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
#             block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
#         )
#
#         self.conv3 = spconv.SparseSequential(
#             # [800, 704, 21] <- [400, 352, 11]
#             block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
#         )
#
#         self.conv4 = spconv.SparseSequential(
#             # [400, 352, 11] <- [200, 176, 5]
#             block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
#         )
#
#         if self.model_cfg.get('RETURN_ENCODED_TENSOR', True):
#             last_pad = self.model_cfg.get('last_pad', 0)
#
#             self.conv_out = spconv.SparseSequential(
#                 # [200, 150, 5] -> [200, 150, 2]
#                 spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
#                                     bias=False, indice_key='spconv_down2'),
#                 norm_fn(128),
#                 nn.ReLU(),
#             )
#         else:
#             self.conv_out = None
#
#         # decoder
#         # [400, 352, 11] <- [200, 176, 5]
#         self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)
#         self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')
#         self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')
#
#         # [800, 704, 21] <- [400, 352, 11]
#         self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)
#         self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')
#         self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')
#
#         # [1600, 1408, 41] <- [800, 704, 21]
#         self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)
#         self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')
#         self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')
#
#         # [1600, 1408, 41] <- [1600, 1408, 41]
#         self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)
#         self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')
#
#         self.conv5 = spconv.SparseSequential(
#             block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')
#         )
#         self.num_point_features = 16
#
#     def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv):
#         x_trans = conv_t(x_lateral)
#         x = x_trans
#         x.features = torch.cat((x_bottom.features, x_trans.features), dim=1)
#         x_m = conv_m(x)
#         x = self.channel_reduction(x, x_m.features.shape[1])
#         x.features = x_m.features + x.features
#         x = conv_inv(x)
#         return x
#
#     @staticmethod
#     def channel_reduction(x, out_channels):
#         """
#         Args:
#             x: x.features (N, C1)
#             out_channels: C2
#
#         Returns:
#
#         """
#         features = x.features
#         n, in_channels = features.shape
#         assert (in_channels % out_channels == 0) and (in_channels >= out_channels)
#
#         x.features = features.view(n, out_channels, -1).sum(dim=2)
#         return x
#
#     def forward(self, batch_dict):
#         """
#         Args:
#             batch_dict:
#                 batch_size: int
#                 vfe_features: (num_voxels, C)
#                 voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
#         Returns:
#             batch_dict:
#                 encoded_spconv_tensor: sparse tensor
#                 point_features: (N, C)
#         """
#         voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
#         batch_size = batch_dict['batch_size']
#         input_sp_tensor = spconv.SparseConvTensor(
#             features=voxel_features,
#             indices=voxel_coords.int(),
#             spatial_shape=self.sparse_shape,
#             batch_size=batch_size
#         )
#         x = self.conv_input(input_sp_tensor)
#
#         x_conv1 = self.conv1(x)
#         x_conv2 = self.conv2(x_conv1)
#         x_conv3 = self.conv3(x_conv2)
#         x_conv4 = self.conv4(x_conv3)
#
#         if self.conv_out is not None:
#             # for detection head
#             # [200, 176, 5] -> [200, 176, 2]
#             out = self.conv_out(x_conv4)
#             batch_dict['encoded_spconv_tensor'] = out
#             batch_dict['encoded_spconv_tensor_stride'] = 8
#
#         # for segmentation head
#         # [400, 352, 11] <- [200, 176, 5]
#         x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4)
#         # [800, 704, 21] <- [400, 352, 11]
#         x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3)
#         # [1600, 1408, 41] <- [800, 704, 21]
#         x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2)
#         # [1600, 1408, 41] <- [1600, 1408, 41]
#         x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5)
#
#         batch_dict['point_features'] = x_up1.features
#         point_coords = common_utils.get_voxel_centers(
#             x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size,
#             point_cloud_range=self.point_cloud_range
#         )
#         batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1)
#         return batch_dict
